Model type
Convolutional neural network; this record is the paper-specific evaluated configuration.
Configuration
DEELIG
DEELIG predicts protein–ligand binding affinity from separately supplied protein and ligand information.
Sources
DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity · Materials and Methods/Novel data set: raw data (paragraph 3); Materials and Methods/Novel data set: raw data (paragraph 2)How it worksEvaluated procedure (conceptual)
Conceptual input–method–output guide. Check the procedure text and linked evaluation for fitted components, additional inputs and exact settings.
Sources
DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity · Materials and Methods/Feature extraction/Protein-pocket features (paragraph 1); Discussion (paragraph 2)At a glance
Biological inputs
High-resolution protein structures and non-peptide ligands, without requiring a docked complex as the user input
Sources
DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity · Abstract (paragraph 1); Introduction (paragraph 3)Outputs
Predicted protein–ligand binding affinity
Sources
DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity · Materials and Methods/Novel data set: raw data (paragraph 3); Materials and Methods/Novel data set: raw data (paragraph 2)limited source coverage · Automated source review, 2026-09-16. All specifications and missing details
Evaluations and results
Release 2026-09-17-d277315f7d76 · 1 evaluation · 1 metric row. Different protocols are not a single leaderboard.
| Metric and finding | Coverage and uncertainty | Evidence |
|---|---|---|
| DEELIG: Protein–ligand binding affinity prediction Source paper reports DEELIG on PDBbind core set. Author-reported evaluation · Evaluation metadata: needs review | ||
| 0.889 Pearson R Unit: unitless · Direction: unknown | Uncertainty: not reported in legacy extract Scored: Not reported · Eligible: Not reported | source checkedDEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity · Table 2, DEELIG row, PDBbind v2016 column Source checking is not independent reproduction. |
How it works
How the evaluated method works
Convolutional networks learn interactions between featurised protein pockets and ligands. The study explores atomic and residue-level representations, including grid-based atomic descriptors.
Sources
DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity · Materials and Methods/Feature extraction/Protein-pocket features (paragraph 1); Discussion (paragraph 2)What was evaluated
The linked evaluation record identifies DEELIG: Protein–ligand binding affinity prediction. Its dataset, split, adaptation and evidence origin remain attached to the reported results.
Sources
DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity · The named evaluation’s methods and comparison table; exact preserved evaluation IDs: evaluation-lit-b3-042Strengths and limitations
Strengths and considerations
- The user-facing procedure does not require a precomputed docked ligand pose.
Sources
DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity · Introduction (paragraph 3); Introduction (paragraph 5)
Limitations and conditions
- The paper’s applicability conditions include high-resolution protein structures and non-peptide ligands; this does not establish unrestricted chemical or receptor coverage.
Sources
DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity · Materials and Methods/Novel data set: raw data (paragraph 3); Materials and Methods/Novel data set: raw data (paragraph 2)
Profile review details
Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction.
Stable record: reported-model-fa2da404b4d08eSpecifications
Inputs, training, access and other details
Explanatory profile: limited source coverage · Automated source review, 2026-09-16. Review applies to the cited claims; unresolved fields are listed below. Numerical results retain their own review status.
| Property | Description and evidence |
|---|---|
| Model type | Convolutional neural network; this record is the paper-specific evaluated configuration.SourcesDEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity · Materials and Methods/Feature extraction/Protein-pocket features (paragraph 1); Discussion (paragraph 2) |
| Architecture / procedure | Convolutional networks learn interactions between featurised protein pockets and ligands. The study explores atomic and residue-level representations, including grid-based atomic descriptors.SourcesDEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity · Materials and Methods/Feature extraction/Protein-pocket features (paragraph 1); Discussion (paragraph 2) |
| Biological inputs | High-resolution protein structures and non-peptide ligands, without requiring a docked complex as the user inputSourcesDEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity · Abstract (paragraph 1); Introduction (paragraph 3) |
| Outputs | Predicted protein–ligand binding affinitySourcesDEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity · Materials and Methods/Novel data set: raw data (paragraph 3); Materials and Methods/Novel data set: raw data (paragraph 2) |
| Parameters | An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. · Not reported in inspected sourcesSources (2)DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity; asadahmedtech/DEELIG README.md · Materials and Methods/Novel data set: raw data; Materials and Methods/Data set refinement; Materials and Methods/Feature extraction; Materials and Methods/Feature extraction/Protein-pocket features; Materials and Methods/Feature extraction/Ligand features; Materials and Methods/Feature extraction/Grid formation; Materials and Methods/Strategies; Materials and Methods/Strategies/Atomic model/Preprocessing; inspected for aggregate parameter count (component sizes are not added without an exact configuration); README.md at pinned repository revision |
| Known versions / configuration | DEELIG is the comparison-table label; that label does not specify an immutable weight revision. · Not reported in inspected sourcesSourcesDEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity · Model identification in the comparison table and corresponding Methods; immutable checkpoint revision is not supplied by the table label. |
| Training data / fitting | An in-house prepared protein–ligand dataset described in the study, with explicitly separated training, validation and test records.SourcesDEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity · Materials and Methods/Strategies/Composite model/Training (paragraph 2); Materials and Methods/Data set refinement (paragraph 2) |
| Context limits | A maximum input/context length for this exact evaluated configuration is not established by the inspected sources. · Not reported in inspected sourcesSources (2)DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity; asadahmedtech/DEELIG README.md · Materials and Methods/Novel data set: raw data; Materials and Methods/Data set refinement; Materials and Methods/Feature extraction; Materials and Methods/Feature extraction/Protein-pocket features; Materials and Methods/Feature extraction/Ligand features; Materials and Methods/Feature extraction/Grid formation; Materials and Methods/Strategies; Materials and Methods/Strategies/Atomic model/Preprocessing; inspected for explicit maximum input length (dataset lengths and family-wide limits are not substituted); README.md at pinned repository revision |
| Access | Official study implementation and usage documentation: https://github.com/asadahmedtech/DEELIG/blob/3a3993fc903c40f1ce904111c8e085c79fb45df6/README.md. This pinned documentation revision is not automatically the evaluated weight revision.Sourcesasadahmedtech/DEELIG README.md · README.md; installation, model download and usage instructions |
| Code licence | No explicit code licence was established from the paper’s availability statement and inspected repository-root documentation. · Not reported in inspected sourcesSourcesasadahmedtech/DEELIG README.md · README.md and repository-root licence-file search |
| Weights licence | The inspected model-access documentation does not explicitly identify terms for this exact evaluated checkpoint or fitted head; repository code terms are shown separately. · Not reported in inspected sourcesSourcesasadahmedtech/DEELIG README.md · README.md; checkpoint/access documentation and licence scope |
Evidence table
Inspect claims, sources and review details
Trace each statement to its source and review. A context-only reference supports the record generally; it does not verify an individual field. Source checking does not reproduce an experiment.
One row per statement and cited source. Multiple citations are not independent evaluations. Shared locators are labelled explicitly.
21 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Diagram caption Conceptual input–method–output guide. Check the procedure text and linked evaluation for fitted components, additional inputs and exact settings. Individual claims | DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity Materials and Methods/Feature extraction/Protein-pocket features (paragraph 1); Discussion (paragraph 2) Version: PMC archival version PMC8274096.1 | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Diagram steps ["High-resolution protein structures and non-peptide ligands, without requiring a docked complex as the user input","DEELIG","Predicted protein–ligand binding affinity"] Individual claims | DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity Materials and Methods/Feature extraction/Protein-pocket features (paragraph 1); Discussion (paragraph 2) Version: PMC archival version PMC8274096.1 | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Diagram title Evaluated procedure (conceptual) Individual claims | DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity Materials and Methods/Feature extraction/Protein-pocket features (paragraph 1); Discussion (paragraph 2) Version: PMC archival version PMC8274096.1 | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Model type Convolutional neural network; this record is the paper-specific evaluated configuration. Individual claims | DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity Materials and Methods/Feature extraction/Protein-pocket features (paragraph 1); Discussion (paragraph 2) Version: PMC archival version PMC8274096.1 | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Architecture / procedure Convolutional networks learn interactions between featurised protein pockets and ligands. The study explores atomic and residue-level representations, including grid-based atomic descriptors. Individual claims | DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity Materials and Methods/Feature extraction/Protein-pocket features (paragraph 1); Discussion (paragraph 2) Version: PMC archival version PMC8274096.1 | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Weights licence The inspected model-access documentation does not explicitly identify terms for this exact evaluated checkpoint or fitted head; repository code terms are shown separately. Individual claims | asadahmedtech/DEELIG README.md README.md; checkpoint/access documentation and licence scope Version: 3a3993fc903c40f1ce904111c8e085c79fb45df6 | unreported automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Biological inputs High-resolution protein structures and non-peptide ligands, without requiring a docked complex as the user input Individual claims | DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity Abstract (paragraph 1); Introduction (paragraph 3) Version: PMC archival version PMC8274096.1 | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Outputs Predicted protein–ligand binding affinity Individual claims | DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity Materials and Methods/Novel data set: raw data (paragraph 3); Materials and Methods/Novel data set: raw data (paragraph 2) Version: PMC archival version PMC8274096.1 | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Parameters An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. Individual claims | DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity Materials and Methods/Novel data set: raw data; Materials and Methods/Data set refinement; Materials and Methods/Feature extraction; Materials and Methods/Feature extraction/Protein-pocket features; Materials and Methods/Feature extraction/Ligand features; Materials and Methods/Feature extraction/Grid formation; Materials and Methods/Strategies; Materials and Methods/Strategies/Atomic model/Preprocessing; inspected for aggregate parameter count (component sizes are not added without an exact configuration); README.md at pinned repository revision Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: PMC archival version PMC8274096.1 | unreported automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Parameters An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. Individual claims | asadahmedtech/DEELIG README.md Materials and Methods/Novel data set: raw data; Materials and Methods/Data set refinement; Materials and Methods/Feature extraction; Materials and Methods/Feature extraction/Protein-pocket features; Materials and Methods/Feature extraction/Ligand features; Materials and Methods/Feature extraction/Grid formation; Materials and Methods/Strategies; Materials and Methods/Strategies/Atomic model/Preprocessing; inspected for aggregate parameter count (component sizes are not added without an exact configuration); README.md at pinned repository revision Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 3a3993fc903c40f1ce904111c8e085c79fb45df6 | unreported automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Sources and history
Release 2026-09-17-d277315f7d76 · Record review: needs review
2 source records and release history
- DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity · Original source · PMC archival version PMC8274096.1
- asadahmedtech/DEELIG README.md · Original source · 3a3993fc903c40f1ce904111c8e085c79fb45df6
Technical metadata and extraction receipts
Stable ID: reported-model-fa2da404b4d08e
- areas
- molecular-interactions
- entity level
- method
- version
- Not reported
- reported name
- DEELIG
- historical missing metadata
- version: not_reported_in_legacy_extract; checkpoint revision: not_reported_in_legacy_extract; training data: not_reported_in_legacy_extract; licence: not_reported_in_legacy_extract
- metadata review scope
- historical_missing_metadata preserves the original discovery state. Current descriptive evidence and missingness are recorded in profile.facts; numerical-result review is separate.
- legacy kinds
- model
- entity classification
- review date: 2026-09-17; rationale: This source-scoped entry preserves the method/configuration actually named in an evaluation. It is neither a global family identity nor proof of an immutable checkpoint; the linked evaluation retains adaptation, fitting and scoring details.; source ids: deelig-2021; source locator: Materials and Methods/Feature extraction/Protein-pocket features (paragraph 1); Discussion (paragraph 2) | Materials and Methods/Novel data set: raw data (paragraph 3); Materials and Methods/Novel data set: raw data (paragraph 2); ambiguities: Configuration means the source-labelled evaluated identity. It does not establish missing checkpoint hashes, default settings or equivalence to same-named records in other papers.